The Complete Guide to Running LLMs Locally in 2026
Running AI models locally gives you privacy, zero API costs, and full control. This guide covers everything from hardware selection to your first inference.
Step 1: Know Your Hardware
The first step is understanding what your GPU can handle. VRAM is the primary constraint — it determines which models and quantizations you can load. Use our hardware checker to instantly see your capabilities.
Step 2: Choose Your Runtime
Ollama — The simplest option. One-command install, one-command run. Best for beginners.
LM Studio — GUI application with model browser and chat interface. Great for non-technical users.
llama.cpp — The engine behind most local inference. Maximum performance and flexibility.
vLLM — For serving models as an API. Best for developers building applications.
Step 3: Pick the Right Quantization
| Quantization | Quality | VRAM Savings | Best For |
|---|---|---|---|
| Q4_K_M | 85% | 75% smaller | Most users, best value |
| Q5_K_M | 90% | 70% smaller | Better quality, still efficient |
| Q8_0 | 98% | 50% smaller | Near-lossless, if VRAM allows |
| FP16 | 100% | None | Maximum quality, needs lots of VRAM |
Step 4: Match Model to Task
Different tasks require different models. A coding assistant needs a code-specialized model, while image generation requires a completely different architecture. Browse our model database to find the right model for your use case.
When Local Isn't Enough
Some models are simply too large for consumer hardware. DeepSeek V3 (685B parameters) needs hundreds of gigabytes of VRAM. For these, cloud GPU services provide on-demand access.